Concept hierarchy memory model: A neural architecture for conceptual knowledge representation, learning, and commonsense reasoning
This article introduces a neural network based cognitive architecture termed Concept Hierarchy Memory Model (CHMM) for conceptual knowledge representation and commonsense reasoning. CHMM is composed of two subnetworks: a Concept Formation Network (CFN), that acquires concepts based on their sensory...
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Main Authors: | , |
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Format: | text |
Language: | English |
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Institutional Knowledge at Singapore Management University
1996
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Online Access: | https://ink.library.smu.edu.sg/sis_research/5225 https://ink.library.smu.edu.sg/context/sis_research/article/6228/viewcontent/Concept20Hierarchy20Memory20Model_IJNS96.PDF.pdf |
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Institution: | Singapore Management University |
Language: | English |
Summary: | This article introduces a neural network based cognitive architecture termed Concept Hierarchy Memory Model (CHMM) for conceptual knowledge representation and commonsense reasoning. CHMM is composed of two subnetworks: a Concept Formation Network (CFN), that acquires concepts based on their sensory representations; and a Concept Hierarchy Network (CHN), that encodes hierarchical relationships between concepts. Based on Adaptive Resonance Associative Map (ARAM), a supervised Adaptive Resonance Theory (ART) model, CHMM provides a systematic treatment for concept formation and organization of a concept hierarchy. Specifically, a concept can be learned by sampling activities across multiple sensory fields. By chunking relations between concepts as cognitive codes, a concept hierarchy can be learned/modified through experience. Also, fuzzy relations between concepts can now be represented in terms of the weights on the links connecting them. Using a unified inferencing mechanism based on code firing, CHMM performs an important class of commonsense reasoning, including concept recognition and property inheritance. |
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